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Record W4386874315 · doi:10.2196/47137

MASI, a Smartphone App to Improve Treatment Adherence Among South African Adolescents and Young Adults With HIV: Protocol for a Pilot Randomized Controlled Trial

2023· article· en· W4386874315 on OpenAlexvenueno aff
Marta I. Mulawa, Jacqueline Hoare, Elizabeth T. Knippler, Bulelwa Mtukushe, Mluleki Matiwane, Kathryn E. Muessig, Maryam Al-Mujtaba, T Harper Wilkinson, Alyssa Platt, Joseph R. Egger, Lisa Hightow‐Weidman

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthCenter for AIDS Research, University of WashingtonNational Institute of Allergy and Infectious DiseasesCenter for AIDS Research, Duke UniversityNational Institutes of Health
KeywordsRandomized controlled trialMedicineProtocol (science)Physical therapyGerontologyPsychologyAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescents and young adults with HIV repeatedly demonstrate low rates of antiretroviral therapy (ART) adherence as well as low rates of viral suppression. Digital health interventions are a promising way to engage adolescents and young adults with HIV to support ART adherence. However, few digital health interventions have been developed and tested with adolescents and young adults in countries like South Africa, where the HIV burden among adolescents and young adults is greatest. Masakhane Siphucule Impilo Yethu (MASI; Xhosa for "Let's empower each other and improve our health") is a comprehensive ART adherence-supporting app for South African adolescents and young adults with HIV. It was culturally adapted using the HealthMpowerment platform. OBJECTIVE: The aim of this paper is to describe the protocol for a pilot randomized controlled trial examining the feasibility, acceptability, and preliminary efficacy of MASI on self-reported ART adherence and social support. METHODS: We will enroll 50 adolescents and young adults with HIV ages 15-21 years. Participants will be recruited from public ART clinics linked to a large government-funded teaching hospital in Cape Town, South Africa. Participants will be randomized 1:1 into either the intervention arm receiving a full version of MASI or the control arm receiving an information-only version of the app (n=25 per arm). Participants will be asked to engage with MASI daily for 6 months. All participants will complete baseline and follow-up assessments at 3 and 6 months. RESULTS: Study screening began in May 2022 and the first participant was enrolled on June 21, 2022. As of June 12, 2023, 81 participants have completed screeners, and 36 eligible participants have been enrolled in the pilot randomized controlled trial. Recruitment is anticipated to last through August 31, 2023, with study activities anticipated through February 29, 2024. CONCLUSIONS: There is an urgent need for innovative interventions to improve ART adherence among adolescents and young adults in settings like South Africa. If found to be feasible and acceptable, MASI could be implemented with adolescents and young adults with HIV in other parts of the country. TRIAL REGISTRATION: ClinicalTrials.gov NCT04661878; https://clinicaltrials.gov/ct2/show/study/NCT04661878. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47137.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.091
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.026
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0910.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.481
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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